[UI76] Pipeline reproduction (AFNI - deriv)

[bclenet]

Softwares

• softwares : Version AFNI_18.1.03, precompiled binaries for ubuntu

Input data

derivatives (fMRIprep)

Additional context

• preprocessing_order : We additionally applied spatial smoothing (up to 4mm FWHM, iterative) and intensity normalization to the data preprocessed with fmriprep.
• intensity_normalization : Run-wise intensity normalization using percentage scaling to the mean value of the entire time series.
• volume_censoring : Framewise displacement, considering the raw metric value (i.e., no binary spikes regressors)

Subject level : 3dDeconvolve with stim_times_AM2 and dmBLOCK
Group level : 3dttest++ and ETAC clustering

List of tasks

Please tick the boxes below once the corresponding task is finished. 👍

• 👌 A maintainer of the project approved the issue, by assigning a 🏁status: ready for dev label to it.
• 🌳 Create a branch on your fork to start the reproduction.
• 🌅 Create a file team_{team_id}.py inside the narps_open/pipelines/ directory. You can use a file inside narps_open/pipelines/templates as a template if needed.
• 📥 Create a pull request as soon as you completed the previous task.
• 🧠 Write the code for the pipeline, using Nipype and the file architecture described in docs/pipelines.md.
• 📘 Make sure your code is documented enough.
• 🐍 Make sure your code is explicit and conforms with PEP8.
• 🔬 Create tests for your pipeline. You can use files in tests/pipelines/test_team_* as examples.
• 🔬 Make sure your code passes all the tests you created (see docs/testing.md).